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Well Academy

A brief introduction to genetic algorithms with examples.

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chriskam1250

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PlayList : Artificial Intelligence : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH
Genetic Algorithm is - Optimization Algorithm
- Based on natural phenomenon
- Nature inspired approach based on Darwin’s law of Survival of the fittest and bio-inspired operators such as Pairing Crossover and Mutation.
- frequently used to find optimal or near-optimal solutions to difficult problems
Optimization- is the process of making something better
Terminology - Population
- Chromosomes
- Gene
Operators are - Selection
- Crossover
- Mutation
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Ask Faizan

Title: Automatic Time Table Generation Using Genetic Algorithm
Domain: Data Mining
Key Features:
1. Generation of time table using genetic algorithm.
2. Time table generation separately for teacher and students.
3. Downloadable in .xls file
4. Facility of curd model for teacher and students, etc.
For more details contact:
E-Mail: [email protected]
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Views: 12428
InnovationAdsOfIndia

Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms.
Support this channel on Patreon: https://patreon.com/codingtrain
Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics
Contact: https://twitter.com/shiffman
Links discussed in this video:
The Nature of Code: http://natureofcode.com/
BoxCar2D: http://boxcar2d.com/
Source Code for the Video Lessons: https://github.com/CodingTrain/Rainbow-Code
p5.js: https://p5js.org/
Processing: https://processing.org
For More Genetic Algorithm videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV
For More Nature of Code videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aFlwukCmDf0-1-uSR7mklK
Help us caption & translate this video!
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Views: 178729
The Coding Train

Get an introduction to the components of a genetic algorithm.
Get a Free MATLAB Trial: https://goo.gl/C2Y9A5
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Learn how genetic algorithms are used to solve optimization problems. Examples illustrate important concepts such as selection, crossover, and mutation. Finally, an example problem is solved in MATLAB® using the ga function from Global Optimization Toolbox.

Views: 109432
MATLAB

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Prashant Badole

If you want to download PPT than you can easily download it now from: http://xraypixy.com
I recreated this video on NFHS with improvement in voice and stuff. and I also added information about machine learning techniques.
- Neural network
- Role of NN in machine learning
- Neural network learning algorithms
- The neural network in pattern recognition, speech and object recognition.
- The Comparision of Neural network with the biological network.
video link(Neuro-Fuzzy Hybrid System (Part-2)- Soft Computing)
https://www.youtube.com/watch?v=Bv7EtS6q6_Q
Soft Computing book for the Neural network:-
https://www.amazon.in/Principles-Soft-Computing-2ed-WIND/dp/8126527412/ref=as_li_ss_tl?s=books&ie=UTF8&qid=1513193138&sr=1-1&keywords=soft+computing&linkCode=sl1&tag=pixy-21&linkId=4bcc64ac49ca9f9acf69edb48db2bbcd
Neuro-Fuzzy Hybrid System
A Hybrid Intelligent System is one that combines at least two intelligent technologies.
For example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system.
Neuro-Fuzzy hybridization is widely termed as Fuzzy Neural Network (FNN) or Neuro-Fuzzy System(NFS).
NFHS is a learning mechanism that utilizes the training and learning algorithms from neural networks to find parameters of a fuzzy system.
The architecture is a three-layer feedforward neural network model.
if you want to ask anything related this video than you can ask me on this page : -
Google+ page:
https://plus.google.com/118027282802701170491
Full artical:
http://pixycomputer.blogspot.com/2017/03/neuro-fuzzy-hybrid-system-in-soft.html
Ritika xRay Pixy
(www.xraypixy.com)

Views: 17055
Ritika Saini

The mechanism for unearthing hidden facts in large datasets and drawing inferences on how a subset of items influences the presence of another subset is known as Association Rule Mining (ARM). There is a wide variety of rule interestingness metrics that can be applied in ARM. Due to the wide range of rule quality metrics it is hard to determine which are the most `interesting' or `optimal' rules in the dataset. In this paper we propose a multi-objective approach to generating optimal association rules using two new rule quality metrics: syntactic superiority and transactional superiority. These two metrics ensure that dominated but interesting rules are returned to not eliminated from the resulting set of rules.

Views: 407
Final Year Solutions

In this tutorial, I will show you how to optimize a single objective function using Genetic Algorithm. We use MATLAB and show the whole process in a very easy and understandable step-by-step process.
For a tutorial on Constrained Optimization with Genetic Algorithm see this video: https://www.youtube.com/watch?v=esGZtl3gIlY&feature=youtu.be

Views: 60964
NKN DNE

Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in

Views: 144071
nptelhrd

Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases
Steps of Classification:
1. Model construction: Describing a set of predetermined classes
Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute.
The set of tuples used for model construction is training set.
The model is represented as classification rules, decision trees, or mathematical formulae.
2. Model usage: For classifying future or unknown objects
Estimate accuracy of the model
If the accuracy is acceptable, use the model to classify new data
MLP- NN Classification Algorithm
The MLP-NN algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer.
Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted and fed simultaneously to a second layer of “neuronlike” units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used.
The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples.
Algorithm of MLP-NN is as follows:
Step 1: Initialize input of all weights with small random numbers.
Step 2: Calculate the weight sum of the inputs.
Step 3: Calculate activation function of all hidden layer.
Step 4: Output of all layers
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E2MATRIX RESEARCH LAB

Cancer Identification System Data Mining Java Project
Download Project Code, Report and PPT
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Views: 770
1000 Projects

Final Presentation for Big Data Analysis

Views: 6965
Qiankun Zhuang

In this video I'm showing how to perform an optimisation procedure in MatLab Simulink using custom requirement
http://join.air.io/mlevinskyi

Views: 3456
Maksym Levinskyi

This tutorial has steps in genetic algorithm

Views: 10213
Red Apple Tutorials

This video quickly describes Fuzzy Logic and its uses for assignment 1 of Dr. Cohen's Fuzzy Logic Class.

Views: 184079
Tim Arnett

Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in

Views: 55708
nptelhrd

In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set.
The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share

Views: 463063
Thales Sehn Körting

fuzzy logic in artificial intelligence in hindi | fuzzy logic example | #28
Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO.
The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO.
The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as −
CERTAINLY YES
POSSIBLY YES
CANNOT SAY
POSSIBLY NO
CERTAINLY NO
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Views: 86836
Well Academy

Fuzzy logic introduction. Fuzzy Set Theory.

Views: 74037
GATE and NET Computer Science video-lec

( Data Science Training - https://www.edureka.co/data-science )
This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv
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Views: 95372
edureka!

Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in

Views: 20266
nptelhrd

Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.

Views: 82706
Harvard University

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.

Views: 346522
Thales Sehn Körting

Hey everyone! This is the first in a series of videos teaching you everything you could possibly want to know about neural networks, from the math behind them to how to create one yourself and use it solve your own problems!
This video is meant to be an a quick intro to what neural nets can do, and get us rolling with a simple dataset and problem to solve.
In the next video I'll cover how to use a neural network to automate the task our farmer character solves manually here.

Views: 141566
giant_neural_network

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SD Pro Engineering Solutions Pvt Ltd.

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PROJECTS2014

Neural network in ai (Artificial intelligence)
Neural network is highly interconnected network of a large number of processing elements called neuron architecture motivated from brain.
Neuron are interconnected to synapses which provide input from other neurons which intern provides output i.e input to other neurons.
Neuron are in massive therefore they provide distributed network.
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lecture 9 - neural networks

Views: 6474
CaelusBot

System uses efficient data mining algorithms to detect and warn user about e banking phishing websites.

Views: 6181
Nevon Projects

In this video we work on an actual sentiment analysis dataset (which is an instance of text classification), for which I also provide Python code (see below). The approach is very similar to something that is commonly called a Naive Bayes Classifier.
Website associated with this video:
http://karpathy.ca/mlsite/lecture2.php

Views: 49698
MLexplained

Introduction to Web Mining and its usage in E-Commerce Websites. This is part 1. This will contain introduction of the field and in part two we will discuss its usage in E-Commerce website.
Please don't forget to give your feedback... :)

Views: 3948
zdev log

Short Interview with Prof. Dr. Thomas Bäck about using Data Mining for Innovations. e.g. genetic / evolutionary algorithms to solve multi-objective problems. Thomas is CEO of DIVIS www.divis-gmbh.de and I met him at the Marcus Evans Conference in Cologne November 2012.

Views: 368
Fabian Schlage

From Ignite at OSCON 2010, a 5 minute presentation by Bill Lavender:
SNORT is popular Network Intrusion Detection System (NIDS) tool that currently uses a custom rule based system to identify attacks. This presentation emphasizes on writing the algorithm to write generate the rules through GA and the integration of them into nProbe, a similar network monitoring tool written by Luca Deri with a plug-in architecture.
Genetic Algorithms are dependent upon identifying attributes to describe a problem and evolving a desired population. In this case, the problem is an attack through the network and identifying the attack through connection property attributes. Genetic Algorithms depends upon training data. DARPA datasets provide training data, in categorized format (attack vs. normal) along with a corresponding raw network recorded format called tcpdump. nProbe has a plug-in architecture allowing for customization.
This presentation explains original code in C to evolve rules. It uses the same chromosome attributes used by Gong. The development verifies and contrasts against the research performed by Gong. It also presents the code for integration into nProbe.
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O'Reilly

very simple explanation about Backpropagation in ANN in hindi.

Views: 11627
Red Apple Tutorials

Contact me on : [email protected]
Neural Networks is one of the most interesting topics in the Machine Learning community. Their potential is being recognized every day as the technology is advancing at an ever growing rate. From being a topic of research for decades to practical use by thousands of organizations, Neural Networks have come a long way. Today there are a number of jobs available in Machine Learning from application to research domain. But Machine Learning is not like conventional programming. It requires a different line of thinking than what conventional programming has taught us.
This might become a problem for people interested in learning Machine Learning. A lot of mathematical concepts are deeply embedded in ML and an understanding of these core concepts will help anyone starting with ML go long way ahead. Trust me! thats the only way.
In this video I have tried to make those core concepts a little bit clearer by using a real-life example. This video is about how simply you can understand the working of an Artificial Neural Network. There are a lot of questions which can come to your mind after watching this video, but do not focus on the "WHY" as much as on the "HOW" of what has been explained.
A detailed explanation of each of the mentioned terms will be covered in the future videos.

Views: 37265
Harsh Gaikwad

Дмитрий Коробченко — руководитель проектов и инженер в области машинного обучения, компьютерного зрения и обработки сигналов в Исследовательском Центре Samsung.
В своей лекции Дмитрий рассказывает о том, что такое машинное обучение, нейронные сети, Deep Learning, о том, какие задачи решают современные технологии машинного обучения и ждет ли нас в будущем настоящий искусственный интеллект.

Views: 7039
E-Contenta

Introduction to fuzzy logic in hindi

Views: 2023
Red Apple Tutorials

( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow )
This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. Motivation Behind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
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How it Works?
1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. Information Architects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at [email protected] or call us at +91 88808 62004 for more information.
Facebook: https://www.facebook.com/edurekaIN/
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LinkedIn: https://www.linkedin.com/company/edureka

Views: 50152
edureka!

Title: Managing Privacy Of Sensitive Attributes Using MFSARNN Clustering With Optimization Technique
Domain: Data Mining
Key Features:
1. The slicing algorithm partitions the data into vertical and horizontal columns. The attributes and tuples in the slicing algorithm is clustered based on their similarity. In vertical partitioning, the attributes are grouped by Modified Fully Self Adaptive Resonance Neural Networks (MFSARNN).
2. The cluster formation has been improved by Genetic Algorithm based feature selection. In the horizontal partition, the tuples are grouped by Meta heuristic Fireflies Algorithm with Minkowsi Distance Measure (MFAMD). In this way the proposed system overcomes the privacy threats such as Identity disclosure, Attribute disclosure and Membership disclosure.
3. Slicing is one of the Anonymization techniques which is used to improve the privacy in data publishing. The slicing algorithm has been working with three different phases like, attribute partitioning, column generalization and tuple partitioning. The slicing algorithm partition the data set into vertical partition and horizontal partition.
4. Cluster analysis is the process of grouping the related information together, which is used for further processing and classification. Clustering techniques work with the high dimensionality of the data and produces the best result without losing the data and it also utilizes the data for entire processing. For this reason, slicing algorithm is working with clustering techniques. Clustering of attributes in the vertical partition is done by using Modified Fully Self Adaptive Resonance Neural Network (MFSARNN).
5. The working of MFSARNN as follows. Initially the sensitive features are selected by using a feature selection method. So, in the proposed system Genetic Algorithm is used for feature selection with three different steps like selection, crossover and mutation. In GA fitness function is one of the most important parameter which is used to select the sensitive features from the data.
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InnovationAdsOfIndia

This tutorial is about Traveling salesman problem example solution using genetic algorithm

Views: 8112
Red Apple Tutorials

This is the demo video for crime aware

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kuma Dm

Final Year Projects | Credit card Fraud Detection using HMM
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ClickMyProject

Genetic Algorithm in MATLAB using Optimization Toolbox. I discussed an example from MATLAB help to illustrate how to use ga-Genetic Algorithm in Optimization Toolbox window and from the command line in MATLAB program.
Amr Abdelnaser
3rd Year (C&E)EE Dept.
Sohag University, Sohag, Egypt

Views: 129089
Amr Abdelnaser

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We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton [18]. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. [8], which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol in [18]. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost.

Views: 1639
Susmay FOURGREENSOFT

© 2018 Banking investment question

Underscoring the seriousness of the undertaking, ASX recently produced an 87-page progress report. Roll-out is targeted for late 2020 or early 2021. In the weeds. The enormity of such a project may not be obvious to those unfamiliar with the creaky plumbing of the capital markets. At the completion of phase one, DTCC will have nodes set up internally for every firm that it knows will run one, plus some general nodes that will take care of supporting the transactions and processing for the firms that do not wish to support a node of their own. For this project, DTCC has taken a multi-vendor approach. Ethereum-inspired startup Axoni is providing the technology, with IBM helping to manage the project, and R3 providing best practice guidance on areas like selecting the right data models. Luxembourg is the largest fund management hub outside of the U.S. The jurisdiction holds many trillions of dollars worth of assets under management. The KPMG-led project includes banks like BNP Paribas, Credit Agricole and others, as well as over 400 asset managers. The technology used is ethereum-based Quorum, the popular open-source project run by JP Morgan.